Improved algorithm for neuronal ensemble inference by **Monte** **Carlo** method

**Monte**

**Carlo**method, and introduce the idea of simulated annealing for hyperparameter control. Expand abstract.

**Monte**

**Carlo**method, and introduce the idea of simulated annealing for hyperparameter control. We also compare the performance of ensemble inference between our algorithm and the original one.

10/10 relevant

arXiv

**Monte** **Carlo** studies of modified scalable designs for quantum computation

**Monte**

**Carlo**simulations are applied to observe the behavior of the system when the parity correction proposal is implemented. The results demonstrate that (1) our parity correction proposal is effective to most of the parity-breaking errors; (2) the lifetime of the qubit benefits from larger island size before it meets the threshold; (3) small chemical potential $ \mu $ on the non-topological backbones and fine tuned paring potential $ \Delta $ of the topological bulk segment are required for high probability of correctness. Our results provide an effective error correction scheme for the parity-breaking errors.

10/10 relevant

arXiv

Space-time multilevel **Monte** **Carlo** methods and their application to
cardiac electrophysiology

**Monte**

**Carlo**methods. Expand abstract.

**Monte**

**Carlo**methods, parallel iterative solvers, and a space-time finite element discretization. This combination allows for space-time adaptivity, time-changing domains, and to take advantage of past samples to initialize the space-time solution. The resulting sequence of problems is distributed using a multilevel parallelization strategy, allocating batches of samples having different sizes to a different number of processors. We assess the performance of the proposed framework by showing in detail its application to the nonlinear equations of cardiac electrophysiology. Specifically, we study the effect of spatially-correlated perturbations of the heart fibers on the mean and variance of the resulting activation map. As shown by the experiments, the theoretical rates of convergence of multilevel

**Monte**

**Carlo**are achieved. Moreover, the total computational work for a prescribed accuracy is reduced by an order of magnitude with respect to standard

**Monte**

**Carlo**methods.

10/10 relevant

arXiv

A new laser-ranged satellite for General Relativity and Space Geodesy
II. **Monte** **Carlo** Simulations and covariance analyses of the LARES 2 Experiment

**Monte**

**Carlo**simulations and covariance analyses fully confirming an error budget of a few parts in one thousand in the measurement of frame-dragging with LARES 2 as calculated in our previous paper. Expand abstract.

**Monte**

**Carlo**simulations and covariance analyses fully confirming an error budget of a few parts in one thousand in the measurement of frame-dragging with LARES 2 as calculated in our previous paper.

10/10 relevant

arXiv

Markov-chain **Monte**-**Carlo** Sampling for Optimal Fidelity Determination in
Dynamic Decision-Making

**Monte**

**Carlo**sampling method is proposed to determine the optimal fidelity of decision making in a dynamic data driven framework. Expand abstract.

**Monte**

**Carlo**sampling method is proposed to determine the optimal fidelity of decision making in a dynamic data driven framework. To evaluate the performance of the proposed method, an experiment is conducted, where the impact of workers performance on the production capacity and the fidelity level of decision making are studied.

10/10 relevant

arXiv

Estimating trajectories of meteors: an observational **Monte** **Carlo**
approach -- I. Theory

8/10 relevant

arXiv

Metal-Insulator and Magnetic Phase Diagram of Ca$_2$RuO$_4$ from
Auxiliary Field Quantum **Monte** **Carlo** and Dynamical Mean Field Theory

**Monte**

**Carlo**and Dynamical Mean Field Theory, to determine the low-temperature phase diagram of Ca$_2$RuO$_4$. Both methods predict a low temperature, pressure-driven metal-insulator transition accompanied by a ferromagnetic-antiferromagnetic transition. The properties of the ferromagnetic state vary non-monotonically with pressure and are dominated by the ruthenium $d_{xy}$ orbital, while the properties of the antiferromagnetic state are dominated by the $d_{xz}$ and $d_{yz}$ orbitals. Differences of detail in the predictions of the two methods are analyzed. This work is theoretically important as it presents the first application of the Auxiliary Field Quantum

**Monte**

**Carlo**method to an orbitally-degenerate system with both Mott and Hunds physics, and provides an important comparison of the Dynamical Mean Field and Auxiliary Field Quantum

**Monte**

**Carlo**methods.

10/10 relevant

arXiv

Modelling an equivalent b-value in diffusion-weighted steady-state free precession

**Monte**-

**Carlo**simulations of non-Gaussian diffusion in DW-SSFP and diffusion-weighted spin-echo (DW-SE) sequences are used to verify the proposed framework. Dependence of ADC on flip angle in DW-SSFP is verified with experimental measurements in a whole, human post-mortem brain. Results:

**Monte**-

**Carlo**simulations reveal excellent agreement between ADCs estimated with DW-SE and the proposed framework. Experimental ADC estimates vary as a function of flip angle over the corpus callosum of the postmortem brain, estimating the mean and standard deviation of the gamma distribution as $1.50\cdot 10^{-4} mm^2/s$ and $2.10\cdot 10^{-4} mm^2/s$. Conclusion: DW-SSFP can be used to investigate non-Gaussian diffusion by varying the flip angle. By fitting a model of non-Gaussian diffusion, the ADC in DW-SSFP can be estimated at an effective b-value, comparable to more conventional diffusion sequences.

5/10 relevant

arXiv

BIMC: The Bayesian Inverse **Monte** **Carlo** method for goal-oriented
uncertainty quantification. Part II

**Monte**

**Carlo**(BIMC) method", was shown to be optimal for problems in which the input-output operator is nearly linear. Expand abstract.

**Monte**

**Carlo**(BIMC) method", was shown to be optimal for problems in which the input-output operator is nearly linear. But applying the original BIMC to highly nonlinear systems can lead to several different failure modes. In this paper, we modify the BIMC method to extend its applicability to a wider class of systems. The modified algorithm, which we call "Adaptive-BIMC (A-BIMC)", has two stages. In the first stage, we solve a sequence of optimization problems to roughly identify those regions of parameter space which trigger the rare-event. In the second stage, we use the stage one results to construct a mixture of Gaussians that can be then used in an importance sampling algorithm to estimate rare event probability. We propose using a local surrogate that minimizes costly forward solves. The effectiveness of A-BIMC is demonstrated via several synthetic examples. Yet again, the modified algorithm is prone to failure. We systematically identify conditions under which it fails to lead to an effective importance sampling distribution.

7/10 relevant

arXiv

Incremental Risk Charge Methodology

**Monte**

**Carlo**simulation is the random draws based on the constant level of risk assumption. Expand abstract.

**Monte**

**Carlo**simulations. The first

**Monte**

**Carlo**simulation simulates default, migration, and concentration in an integrated way. Combining with full re-valuation, the loss distribution at the first liquidity horizon for a subportfolio can be generated. The second

**Monte**

**Carlo**simulation is the random draws based on the constant level of risk assumption. It convolutes the copies of the single loss distribution to produce one year loss distribution. The aggregation of different subportfolios with different liquidity horizons is addressed. Moreover, the methodology for equity is also included, even though it is optional in IRC.

6/10 relevant

SocArXiv